Method for the determination of a time, location, and quantity of goods to be made available based on mapped population activity

ABSTRACT

A population activity mapping method may include detecting a plurality of wireless mobile devices within a geographic region. Individual wireless mobile devices may include a processor, a user interface, a transmitter and a receiver. The detecting operation may be performed by use of a wireless access point, a GPS satellite, and/or a base station, and may be performed at at least two different points in time. Input data may be provided based upon the detecting operation. A distance and speed at which the mobile devices travel within the geographic region may be discerned dependent upon the input data. The discerning operation is performed by at least one processor of a computer network. A time and/or location at which salable output is to be made available and/or an amount of salable output to be made available may be determined dependent upon the discerning operation.

BACKGROUND

Providing goods and/or services to a group of people at a particulartime and place presents numerous logistical problems. A particularproblem that arises is that some customers demand prompt service orotherwise a sales opportunity may be lost. A large number of businessesand other agencies provide goods and services that are valuable toconsumers only when they can be provided at a proper time and place.Moreover, these goods and services may call for some advance planningimmediately prior to providing the goods or services to such customers.This may be a particular problem when dealing with crowds, e.g., whenlarge numbers of potential customers demand prompt service at a giventime, and if no such service is provided, then opportunities to be aservice provider may be lost.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

The foregoing and other features of the present disclosure will becomemore fully apparent from the following description and appended claims,taken in conjunction with the accompanying drawings. Understanding thatthese drawings depict only several embodiments in accordance with thedisclosure and, therefore, are not to be considered limiting of itsscope, the disclosure will be described with additional specificity anddetail through use of the accompanying drawings.

In the drawings:

FIG. 1 is a block diagram of an example arrangement for determiningand/or collecting the location of a mobile device;

FIG. 2 is a block diagram of an example telemetrics-based locationand/or tracking arrangement;

FIG. 3 is a block diagram illustrating an example computing device thatmay be arranged for telemetrics-based location and/or tracking;

FIG. 4 is a flow chart showing the operation of an example populationactivity mapping method; and

FIG. 5 is a diagram of a map, all arranged in accordance with at leastsome embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe Figures, may be arranged, substituted, combined, and designed in awide variety of different configurations, all of which are explicitlycontemplated and make part of this disclosure.

This disclosure is drawn, inter alia, to methods and systems related totelemetrics-based location and/or tracking technology. An exampleembodiment may relate to determining the locations of wireless devices(e.g., cell phones), and this information may be used in conjunctionwith population density maps, population activity maps, and/ortransaction likelihood maps, in order to match-up clients and/orvendors.

This disclosure may include methods and systems for providing details ofwhere crowds of people are located, how the crowds are changing, wherethey are moving to, when they are transitioning from one activity toanother, and/or what activity they are transitioning to. Because thepeople in the crowd may want or need to purchase a provider's goods orservices, it may be valuable to the provider to know such informationabout the crowd.

There are many example applications of the present disclosure which mayenable goods and services to be provided in a better and/or moreresponsive fashion by virtue of having determined and/or reported acrowd's location, pattern of movement, and/or activity level. In oneexample, a taxi service may be informed of when and how many passengerswill be arriving, are arriving, and/or have arrived at an airport, busstation or train station so that the taxi service may dispatch anappropriate number of taxis at an appropriate time to the airport, busstation or train station. The taxi service may be informed of how manypassengers are arriving, how many passengers are exiting the airport,bus station or train station as opposed to making connections, and/orwhich exits the passengers are using. In addition, the taxi service maybe informed of when and how many passengers have arrived at baggageclaim areas. The earlier and more accurately such crowd information canbe predicted and/or provided, the greater the number of taxi fares thatmay be received with less time waiting on the part of the taxi drivers.

In another example, outdoor food vendors may be informed of when exactlycrowds begin to leave theaters or when office workers begin leaving forlunch. Being armed with such information, the food vendors may be betterable to prepare and provide appropriate amounts of food at appropriatetimes.

In another example, a police force may be informed of the distributionof people around a city and/or changes in activity levels in specificlocations around a city. Based on this information, the police force maybetter position its patrol officers to locations around the city wherethe officers may be needed.

In another example, a city government may be informed of the number ofattendees at a city-sponsored event. Thus, the city government may gaugethe level of the citizen's interest in the event.

In yet another example, an ambulance service may be informed of thelocations of people, and consequently their vehicles, on the roadways.The ambulance service may use this information to create a trafficcongestion model in order to determine the best, most uncongested,and/or quickest route for an ambulance to take to the location of anemergency, and/or from the location of the emergency to a hospital.

The present disclosure contemplates that a modern population ofconsumers may include a substantial and relatively predictablepercentage of people who possess a mobile phone or other wireless devicethat may be in contact with a network, such as a wide area network. Thedisclosure may provide techniques that may be used to determine and/orreport the locations of each of these terminal devices. These techniquesmay include GPS-based location determination techniques and/orWi-Fi-based or cell-tower-based location determination techniques, whichmay involve triangulation.

Once the individual location data is gathered, aggregation algorithmsmay be used to create a model of the distribution of the locations ofthe mobile device users. Population estimation models may be used todetermine or estimate size and location of crowds based on thisaggregated information. Demographic information about each of the usersmay be collected, and this demographic information may be used to deriveor estimate characteristics of the crowds, such as the number of men orwomen in a certain age group.

The changing locations of the mobile device users may continue to bemonitored, and the motion of individual terminals may be aggregated toestimate the movement of a crowd or to estimate changes in activitylevels. Alternatively, the flow of terminals from one geographic cell orarea to another may be used to estimate motion or activity.

Instead of tracking the changing locations of individual terminals,changes in the terminal locations as a group, regardless of theirindividual identities or individual motions, may be monitored. Thus,“snapshots” of the group locations may be taken at periodic timeintervals without regard to the identities of the individual terminals.

Regardless of whether the aggregated information relates to individualterminals or only to a group of the terminals as a whole, the aggregatedinformation may be provided to providers of goods and/or services. Theaggregated information may be provided directly to providers of goodsand/or services in an unfiltered state. Alternatively, there may beapplied an analysis protocol that may determine which information is ofinterest to which provider. Thus, each provider may receive only thefiltered information in which he is interested or is willing topurchase. The providers of goods and/or services may then use thefiltered or unfiltered information to decide the location, timing and/orquantity of goods and services to provide.

FIG. 1 is a block diagram of an example arrangement 100 for determiningand/or collecting the location of a mobile device, which is arranged inaccordance with at least some embodiments of the present disclosure. Theexample arrangement 100 includes a mobile device 112 ₁ which a user maycarry with him or on his person. Mobile device 112 ₁ may be a cell phoneand/or another form of wireless device which may include a radioreceiver, radio transmitter, processor and/or user interface. Mobiledevice 112 ₁ may include a built-in GPS receiver and may be incommunication with satellites 114 ₁, 114 ₂, 114 ₃ and 114 ₄. Mobiledevice may determine its global geographic coordinates via communicationwith the satellites in conjunction with trilateration and/or othertechniques. Mobile device 112 ₁ may then wirelessly communicate itslocation to a central office 116 or other centralized depository ofmobile device location information. Central office 116 may becommunicatively coupled to a memory device 120 which may store mobiledevice locations.

In another embodiment in which the mobile device is not GPS-equipped,the mobile device may communicate with cell phone towers to determineits approximate global location and transmit the location to the centraloffice. It is also possible for one or more of the cell phone towers orthe wireless service provider company to determine the location of themobile device and transmit the location to the central office. Forexample, the iPhone 3G from Apple Computer can determine its approximatelocation using either GPS or a combination of proximate wireless accesspoints.

FIG. 2 is a block diagram of an example telemetrics-based locationand/or tracking arrangement 200 including mobile device 112 ₁ andcentral office 116, which were described above with regard to FIG. 1, aswell as other mobile devices 112 ₂, 112 ₃, . . . , 112 _(n) (where n isany number), a goods and/or services provider 118, and a memory device120 storing demographic and historical information. Each of the n numberof mobile devices 112 ₁, 112 ₂, 112 ₃, . . . , 112 _(n) may determineits location via communication with GPS satellites and/or cell phonetowers, and then wirelessly transmit its identity and its location tocentral office 116.

Central office 116 may store the received mobile device locations inmemory device 120. In one embodiment memory device 120 may store mobiledevice locations on a first in first out basis such that only the mostrecent locations are stored. In another embodiment, historical locationdata that is over a few hours old may be compressed to store onlyrepresentative location data and/or sampled location data. For example,memory device 120 may store one to three locations that each mobiledevice spent the most time at during each day in the past.

Central office 116 may also store demographic information related toeach of the people who carry mobile devices 112. Central office 116 mayreceive such demographic information from mobile device carriercompanies that bill the people who carry mobile devices 112 for theiruse of the wireless network. Alternatively, or in addition, centraloffice 116 may receive such demographic information directly from theowners of mobile devices 112 and/or from third parties.

Central office 116 may be communicatively coupled to a data aggregationmodule 117. Central office 116 may store and run aggregation algorithmson the new location data from mobile devices 112 and/or on thedemographic and historical data from memory device 120. The output ofthe aggregation algorithms may include a model of the distribution ofthe locations of mobile devices 112. This model may be used by centraloffice 116 to estimate the size and/or location of crowds including theusers of mobile devices 112. The demographic information retrieved frommemory device 120 may be used to derive and/or estimate characteristicsof the crowds represented by mobile devices 112, such as the number ofmen or women broken down by age groups, monetary income levels, and/orwhere the people live (which may be used as a proxy for where they aregoing).

Central office 116 may transmit the crowd information, which may includethe crowd's demographics, number of people, locations, and/or patternsof movement, to goods and/or services provider 118. Provider 118 maythen estimate the demands of the crowd for the provider's goods and/orservices, including quantities and/or times, based at least in part onthe received crowd information. Hence, provider 118 may prepare tosupply a level or number of goods and/or services that corresponds to,or is appropriate for, the anticipated demands of the crowd.

Many central offices may be provided, and individual central offices 116may be associated with certain respective geographic areas. In oneembodiment, each geographic area may measure about a square mile, whichmay correspond to an area that the crowd is expected, during the nextone to two hours, to traverse on foot, and/or to purchase goods and/orservices within. Central office 116 may filter the crowd information ona geographic basis, and thus use, or transmit to provider 118, only thecrowd information that is of interest to provider 118. For example,central office 116 may transmit to provider 118 information only aboutmobile devices 112 that are within a half-mile radius of provider 118.

In one embodiment, central office 116 may be in communication with onlymobile devices 112 that are within the geographic area with whichcentral office 116 is associated. In another embodiment, mobile devices112 may be in communication with their corresponding wireless servicecarriers, and the carriers may determine the locations of mobile devices112. Each of the wireless service carriers may then send to each centraloffice 116 only information about mobile devices 112 that are within thegeographic area with which that particular central office 116 isassociated.

In another embodiment, central office 116 may include a wireless accesspoint in a retail store, library, and/or other public place. Mobiledevices 112 may connect with the wireless access point only within arange of about one hundred meters, and thus the locations of theindividual mobile devices 112 may not need to be specified with anygreater precision. However, in this embodiment, central office 116 maystill receive demographic information from a wireless carrier or othersource about the mobile devices that are in communication with thewireless access point.

Static Population Density Map

In one embodiment contemplated by the present disclosure, a staticpopulation density map may be provided. A first operation of thisprocess may include determining the locations of individual accessiblephones and/or data terminal customers. For example, mobile devices 112within the geographic area of a central office 116, and/or the wirelesscarriers of such mobile devices, may report the exact locations of themobile devices within the geographic area to central office 116.

As alluded to above, determining the location of each accessible phoneand/or data terminal customer may possibly involve aggregating data frommultiple sources. For example, data from the wireless service carriers,cell phone towers, third parties connected to the wireless servicecarriers or cell phone towers, and/or from the mobile devices themselvesmay be collected and integrated together by central office 116.

Another operation of the static population density map process mayinvolve assigning the detected mobile device locations to geographicregions. For example, each central office 116 may be associated with oneor more respective geographic regions, such as an area in whichpotential common customers and/or clients of retailers within thegeographic region may be congregated. However, it is to be understoodthat one central computer system may be arranged to create the maps andthe associated data structures for many or all of the regions. Such acentral computer system may be communicatively coupled to each of aplurality of central office's 116. In one embodiment, the geographicregion may be a set rectangular area within a city, such as a one mileby one mile square. In other embodiments, the geographic region may bedefined at least in part by barriers to travel (e.g., foot travel), suchas a river, highway, lake, private property, fence, and/or difficultterrain, for example. Thus, central office 116 may determine in which ofthe geographic regions that each mobile device 112 is disposed.

Yet another operation of the static population density map process mayinvolve estimating the population of interest for individual regions.For example, not all people, and not all people carrying a mobile device112, may realistically qualify as a potential client and/or customer forevery product and/or service. The pool of people in the region may befiltered based upon the time-of-day, day of the week, calendar date,historical information, and/or demographic information to identifypeople who have above a threshold level of likelihood of purchasing theparticular goods and/or services of a provider 118. In one embodiment,the population of interest may be estimated by accessing storeddemographic and/and historical information about each detected client.For example, central office 116 may retrieve demographic and/andhistorical information about mobile devices 112 from memory device 120.The historical information may include a number of times, and/or afrequency with which, a particular mobile device 112 has visitedprovider 118.

Estimating the population of interest may also involve filtering and/orweighting detected potential clients according to search criteria. Forexample, the detected population of mobile device 112 users may bebroken down by the sex, age, income level, and/or place of residence ofthe users. Estimating the population of interest may further involveapplying an estimation function to predict the actual potential customerbase. This function may depend on: detected client locations; clientdemographic and historical information; source of client location data;and/or the day of week and time-of-day. For example, a formula or lookuptable may be used to estimate an expected level of sales for individualsdetected within the region. Variables in the formula/lookup table mayinclude the current location of the person, his demographic and/orhistorical location information, how reliable the source of the clientlocation data is, and/or the day of the week, time-of-day, and/or seasonof the year. The formula/lookup table may be based on and/or derivedfrom historical sales data, which data may relate to any of thevariables and/or parameters used in the formula.

In one example, a taxi company may have derived a formula based onhistorical data for the likelihood that an individual at an airport willhail a cab. According to the formula, the likelihood may be estimated asthe sum total of four parameters that depend on the above-describedvariables. For instance, the first parameter may be 0.02 if the personis at a gate area of the airport, and 0.07 if the person is at a baggageclaim area. The second parameter may be 0.03 if the person lives in thestate, and 0.08 if he does not. The third parameter may be 0.06 if theuser location data was received from a wireless service provider, and0.03 if received from a less reliable third party. The fourth parametermay be 0.05 on a weekday, and 0.03 on a weekend. Thus, for a personcurrently at the gate area (0.02), who lives in the state (0.03), whoseinformation was received from a third party (0.03), and for a weekday(0.05), the formula may indicate a probability of 0.13, or 13 percent,that the person will attempt to hail a cab. By summing the estimatedprobabilities for individuals determined to be in the region (e.g.,airport), the taxi company may estimate the number of taxi cabs that maybe needed at the airport. Thus, for example, if 1,000 people aredetermined to be at the airport, and individuals have, on average, a 13%likelihood of hailing a cab, then it may be estimated that 130 taxi cabsmay be needed at the airport during some period of time. A messagerelated to the estimated sales level may be transmitted to a userinterface associated with the taxi company, such as a printer, displaymonitor, wireless mobile device, and/or email account, for example.

A further operation of the static population density map process mayinvolve providing goods or services by determining the appropriatelocation for each service provider based on the predicted customer baseand/or determining the appropriate quantity of service providers,service activity, and/or goods to be provided at individual locations ofinterest based at least in part on the predicted customer base proximateto that location. Still using the taxi company as an example, if a cityhas two airports needing taxi service, the demand at both airports maybe considered when dispatching taxi cabs to one airport or the other.For example, if 120 taxis are needed at airport A and 80 taxis areneeded at airport B, but the company has only 150 taxis, then thecompany may dispatch 90 of the taxis (60%) to airport A and 60 (40%) ofthe taxis to airport B. Continuing this example, if fares at airport Aare historically higher than fares at airport B, and/or have a higherprofit margin, then the company may dispatch 120 taxis to airport A andthe 30 remaining taxis to airport B. If profitability warrants, the taxicompany may even dispatch more than 120 taxis to airport A to increasethe probability that no available fares are missed at airport A in theevent that the estimate of 120 needed taxis turns out to be low.

Population Activity Map I

In one embodiment contemplated by the present disclosure, a populationactivity map may be provided. A first operation of this process mayinclude determining the location of individual accessible phone and/ordata terminal customers. For example, mobile devices 112 within thegeographic area of a central office 116, and/or the wireless carriers ofsuch mobile devices, may report the exact locations of the mobiledevices within the geographic area to central office 116.

As alluded to above, determining the location of individual accessiblephone and/or data terminal customer may possibly involve aggregatingdata from multiple sources. For example, data from the wireless servicecarriers, cell phone towers, third parties connected to the wirelessservice carriers or cell phone towers, and/or from the mobile devicesthemselves may be collected and/or integrated together by central office116.

Another operation of the population activity map process may involvecomparing the location of individual accessible phone and/or dataterminal customers to prior locations. For example, the current locationof a mobile device 112 may be compared to an immediately previouslocation of that same mobile device 112.

Yet another operation of the population activity map process may involveusing the displacement of individual customers and/or the time betweenmeasurements to determine an activity level for individual customers.For example, the current location of individual mobile devices 112 maybe compared to their immediately previous locations to determine trendsin where people are going. The time between measurements, coupled withthe location displacement of the people, may indicate the speed and/ordirection in which the people are moving, and hence the time at whichthey may be able to reach provider 118. The speed at which people movemay also be used as an indication of their level of conviction in movingin their current direction. For instance, the faster people move, themore likely it may be that they will continue moving in same directionin which they are currently moving.

Still another operation of the population activity map process mayinvolve assigning the activity levels to regions. For example,individual central offices 116 may be associated with respectivegeographic regions, such as an area in which potential common customersand/or clients of retailers within the geographic region may becongregated. In one embodiment, the geographic region may be apredetermined rectangular area within a city, such as a one mile by onemile square. In other embodiments, the geographic region may be definedat least in part by barriers to travel (e.g., foot travel), such as ariver, highway, lake, private property, fence, and/or difficult terrain,for example. Thus, central office 116 may determine in which of thegeographic regions that individual moving mobile devices 112 aredisposed. In one embodiment, only those mobile devices 112 moving at atleast a minimum threshold speed and/or within a range of directions maybe assigned to geographic regions.

A further operation of the population activity map process may involveestimating the activity level of a population of interest for eachregion. For people who realistically qualify as a potential clientand/or customer for a particular provider 118, their direction,frequency, speed and/or degree of movement may be determined at leastpartially by comparing current locations to previous locations atcertain times in the past. The previous locations may include theimmediately preceding location and/or locations that were determinedfurther back in time. In one embodiment, the activity level of apopulation of interest may be estimated by accessing stored demographicand/or historical information about individual detected clients. Forexample, central office 116 may retrieve demographic and/or historicalinformation about mobile devices 112 from memory device 120. Thehistorical information may include previous locations of individualmobile devices 112.

Estimating the activity level of a population of interest may alsoinvolve filtering and/or weighting detected potential clients accordingto search criteria. For example, the detected population of mobiledevice 112 users may be broken down by the sex, age, income level,and/or place of residence of the users. Estimating the activity level ofa population of interest may further involve applying an estimationfunction to predict the actual potential customer base. This functionmay depend on: detected client locations; client demographic andhistorical information, the source of client location data, the day ofweek and time-of-day, the activity level of each client, the aggregateactivity level within each region, and/or the change in activity levelover time. For example, a formula and/or lookup table may be used toestimate respective expected levels of sales for individuals detectedwithin the region. Variables in the formula/lookup table may include thecurrent location of the person, his demographic and/or historicallocation information, how reliable the source of the client locationdata is, the day of the week, time-of-day, and/or season of the year,the person's level of movement, the average and/or composite activitylevel of the potential clients within a region, and/or a change in alevel of activity of individuals and/or groups of people over time. Theformula/lookup table may be at least partially based on and/or derivedfrom historical sales data, which data may relate to any of thevariables and/or parameters used in the formula.

In one example, a restaurant selling pizza by the slice may have deriveda formula based on historical data for the likelihood that an individualwithin a half-mile radius of the restaurant will purchase a slice ofpizza within the restaurant within the next ten minutes. According tothe formula, the likelihood may be estimated as the sum total of sevenparameters that depend on the above-described variables. For instance,the first parameter may be 0.0002 if the person is in a shopping mall afew blocks away, 0.0011 if the person is in a movie theater one blockaway, and/or 0.0006 if the person is in an area between the mall and thetheater. The second parameter may be 0.0003 if the person lives in acounty, and 0.0008 if he does not. The third parameter may be 0.0006 ifthe user location data was received from a wireless service provider orcell phone tower, and 0.0003 if received from a less reliable thirdparty. The fourth parameter may be 0.0007 during a one hour lunch timeor a one hour dinner time, and 0.0003 at all other times. The fifthparameter may be 0.0008 if the person is walking at a pace of at leastthree miles an hour and is getting closer to the restaurant, and 0.0001if he does not meet these qualifications. The sixth parameter may be0.0007 if the crowd of potential customers, on average, is closer to therestaurant than it was ten seconds ago, and 0.0005 if it is not. Theseventh parameter may be 0.0009 if an individual, or the crowd ofpotential customers, on average, is moving faster than it was thirtyseconds ago, and 0.0004 is he/they is/are not. Thus, assuming that it isdetermined that a person is in the theater (0.0011), he lives in thecounty (0.0003), his information was received from a wireless serviceprovider (0.0006), it is currently during the lunch hour (0.0007), theperson is not getting closer to the restaurant (0.0001), the crowd iscloser than they were ten seconds ago (0.0007), and the person is movingfaster than he was thirty seconds ago (0.0009), then the formula mayindicate a probability of 0.0044, or 0.44 percent, that the person willvisit the restaurant for a slice of pizza within the next ten minutes.By summing the estimated probabilities for each person determined to bein the region (e.g., within a half-mile of the restaurant), therestaurant may estimate the number of pizza slices that will be neededwithin the next ten minutes. Thus, for example, if 10,000 people aredetermined to be within a half-mile of the restaurant, and each personhas, on average, a 0.37% likelihood of ordering a slice of pizza, thenit may be estimated that 37 slices of pizza will be needed in the nextten minutes.

A further operation of the population activity map process may involveproviding goods and/or services by: determining the appropriate time toprovide the goods and services based on activity level and/or changes inactivity level; determining the appropriate location for individualservice providers based at least partially on the predicted customerbase; and/or determining the appropriate quantity of service providers,service activity, and/or goods to be provided at individual locations ofinterest based at least in part on the predicted customer base proximateto that location. Using the example of a taxi company operating in theregion of the above-described pizza restaurant, the company may decideto send additional taxis to the region within five minutes if it isdetermined that there is an increasing level of movement from arestaurant district within the region towards a theater district withinthe region. In addition, the taxi company may decide to send theadditional taxis specifically to an area between the restaurant districtand the theater based at least in part on a predicted need in that area.Further, the taxi company may decide on a number of taxis to dispatch tothe region based at least partially on a number of people currently inthe region who meet demographic qualifications, e.g., income level.

Population Activity Map II

In one embodiment contemplated by the present disclosure, a populationactivity map may be provided, but in a process that may be different insome respects from the Population Activity Map I process describedabove. A first operation of this process may include determining thenumber of phones and/or wireless data terminals in contact with a singlecell and/or access point for individual such cells and/or access pointsin a mapped area. For example, individual wireless access points and/orbase stations in a geographic region may determine and/or continuouslyupdate the number of mobile devices that the access point is incommunication with at any time.

As alluded to above, determining the number of phones and/or wirelessterminals in a mapped area may possibly involve aggregating data frommultiple sources. For example, data from individual wireless accesspoints and/or base stations within the mapped area may be gathered by acentral office 116.

Another operation of the population activity map process may involvecreating a client flow map that represents the identity, speed, anddirection of client flow from one base station region to another.Creating the client flow map may involve using: the change in the numberof client terminals in contact with individual base station, informationabout clients that move from contact with one base station to another,information about clients that add contact to a new base station, andinformation about clients that lose contact with one of a plurality ofbase stations. For example, the client flow map may be a mathematicalrepresentation of movements of groups of individuals, and/or theirdemographics, between regions within a mapped area.

Yet another operation of the population activity map process may involveusing the flow information of individual customers and/or the timebetween measurements to determine an activity level for an individualcustomer. For example, the current locations of individual mobiledevices 112 may be compared to their immediately previous locations todetermine trends in where people are going. The time betweenmeasurements, coupled with the location displacement of the people, mayindicate the speed and/or direction in which the people are moving, andhence the time at which they may be able to reach provider 118. Thespeed at which people move may also be used as an indication of theirlevel of conviction in moving in their current direction. For instance,the faster people move, the more likely it may be that they willcontinue moving in same direction in which they are currently moving.

Other operations of the Population Activity Map II process may involveassigning activity levels to regions, estimating the activity level of apopulation of interest for each region and/or providing goods and/orservices. These three operations may be substantially similar to thecorresponding operations described above with respect to the PopulationActivity Map I process, and thus are not described in detail here inorder to avoid needless repetition.

Population Activity Map III

In another embodiment contemplated by the present disclosure, apopulation activity map may be provided, but in a process that may bedifferent in some respects from the Population Activity Map I and IIprocesses described above. A first operation of this process may includedetermining the number of phones and/or wireless data terminals incontact with a single cell and/or access point for individual such cellsand/or access points in a mapped area. For example, individual wirelessaccess points and/or base stations in a geographic region may determineand/or continuously update the number of mobile devices that the accesspoint is in communication with at any time.

As alluded to above, determining the number of phones and/or wirelessterminals in a mapped area may possibly involve aggregating data frommultiple sources. For example, data from individual wireless accesspoints and/or base stations within the mapped area may be gathered by acentral office 116.

Another operation of the population activity map process may involvecreating a client flow map that represents the quantity of customers,average speed, and/or direction of client flow from one base stationregion to another. Creating the client flow map may involve using: thechange in the number of client terminals in contact with individual basestations, information about clients that move from contact with one basestation to another, information about clients that add contact to a newbase station, and/or information about clients that lose contact withone of a plurality of base stations. For example, the client flow mapmay be a mathematical representation of movements of groups ofindividuals, including their average speed and/or direction of movement,between regions within a mapped area.

Yet another operation of the population activity map process may involveusing the flow information within each region to determine an activitylevel for that region. For example, the average location, and/or centerof gravity, of the group of mobile devices 112 may be compared to aprevious (such as its immediately previous) average location todetermine trends in where people are going. The time betweenmeasurements, coupled with the location displacement of the group ofpeople, may indicate the speed and/or direction in which the group ofpeople are moving, and hence the time at which they may be able to reachprovider 118, on average.

A further operation of the population activity map process may involveproviding goods and/or services by: determining the appropriate time toprovide the goods and/or services based at least in part on activitylevel and/or changes in activity level; determining the appropriatelocation for individual service providers based at least partially onthe activity level; and/or determining the appropriate quantity ofservice providers, service activity, and/or goods to be provided atindividual locations of interest based at least in part on the activitylevel proximate to that location. Again using the example of a taxicompany operating in the region of the above-described pizza restaurant,the company may decide to send additional taxis to the region withinfive minutes if it is determined that there is an increasing level ofmovement from a restaurant district within the region towards a theaterdistrict within the region. In addition, the taxi company may decide tosend the additional taxis specifically to an area between the restaurantdistrict and the theater based on a predicted need in that area.Further, the taxi company may decide on a number of taxis to dispatch tothe region based on a number of people currently in the region.

Transaction Likelihood Map

In another embodiment contemplated by the present disclosure, atransaction likelihood map may be provided. A first operation of thisprocess may include determining the location of individual accessiblephone and/or data terminal customers. For example, mobile devices 112within the geographic area of a central office 116, and/or the wirelesscarriers of such mobile devices, may report the exact locations of themobile devices within the geographic area to central office 116.

As alluded to above, determining the location of individual accessiblephone or data terminal customers may possibly involve aggregating datafrom multiple sources. For example, data from the wireless servicecarriers, cell phone towers, third parties connected to the wirelessservice carriers or cell phone towers, and/or from the mobile devicesthemselves may be collected and/or integrated together by central office116.

Another operation of the transaction likelihood map process may involvecomparing the location of individual accessible phone and/or dataterminal customers to prior locations. For example, the current locationof a mobile device 112 may be compared to an immediately previouslocation of that same mobile device 112.

Yet another operation of the transaction likelihood map process mayinvolve using the displacement of individual customers and/or the timebetween measurements to determine an activity level for individualcustomers. For example, the current location of individual mobiledevices 112 may be compared to their previous (such as immediatelyprevious) locations to determine trends in where people are going. Thetime between measurements, coupled with the location displacement of thepeople, may indicate the speed and/or direction in which the people aremoving, and hence the time at which they may be able to reach provider118. The speed at which people move may also be used as an indication oftheir level of conviction in moving in their current direction. Forinstance, the faster people move, the more likely it may be that theywill continue moving in same direction in which they are currentlymoving.

Still another operation of the transaction likelihood map process mayinvolve using the activity level of individual customers and/or thedirection of motion to predict a time and/or place of a likelytransaction with individual customers. For example, the speed and/ordirection in which a particular mobile device 112 is currently movingmay be extrapolated to determine a time at which mobile device 112 willbe closest to provider 118, and that time may be deemed the most likelytime at which the owner of mobile device 112 will make a purchase fromprovider 118. If provider 118 is also mobile, then the prediction maytake into account future movements by provider 118 intended to cause thepaths of mobile device 112 and provider 118 to intersect in space andtime such that mobile device 112 and provider 118 may engage each other.

A further operation of the transaction likelihood map process mayinvolve assigning the likely transactions to regions and time periods.For example, individual central offices 116 may be associated withrespective geographic regions, such as an area in which potential commoncustomers and/or clients of retailers within the geographic region maybe congregated. In one embodiment, the geographic region may be apredetermined rectangular area within a city, such as a one mile by onemile square. In other embodiments, the geographic region may be definedat least in part by barriers to travel (e.g., foot travel), such as ariver, highway, lake, private property, fence, and/or difficult terrain,for example. Thus, central office 116 may determine in which of thegeographic regions that individual moving mobile devices 112 will bedisposed during a time period in which mobile devices 112 are mostlikely to make a transaction with provider 118. In one embodiment, onlythose mobile devices 112 having at at least a minimum thresholdlikelihood of making a purchase from provider 118 may be assigned togeographic regions and time periods.

A further operation of the transaction likelihood map process mayinvolve estimating the transaction likelihood of a population ofinterest for each region and time period. In one embodiment, theactivity level of a population of interest may be estimated by accessingstored demographic and historical information about each detectedclient. For example, central office 116 may retrieve demographic and/orhistorical information about mobile devices 112 from memory device 120.The historical information may include previous locations of individualmobile devices 112.

Estimating the transaction likelihood of a population of interest mayalso involve filtering and/or weighting detected potential clientsaccording to search criteria. For example, the detected population ofmobile device 112 users may be broken down by the sex, age, incomelevel, and/or place of residence of the users. Estimating thetransaction likelihood of a population of interest may further involveapplying an estimation function to predict the probability of atransaction for individual regions and time periods. This function maydepend on: detected client locations during the time period, historicaltransaction rates for individual regions in individual time periods,client demographic and/or historical information, source of clientlocation data, the day of week and/or time-of-day, the activity level ofindividual clients, the aggregate activity level within each region,and/or the change in activity level over time. For example, a formulaand/or lookup table may be used to estimate an expected level of salesduring a given time period for individuals detected within the region.Variables in the formula/lookup table may include the current locationof the person, the historical sales made in the region during analogouspast time periods, the person's demographic and historical locationinformation, how reliable the source of the client location data is, theday of the week, time-of-day, and/or season of the year, the person'slevel of movement, the average or composite activity level of thepotential clients within a region, and/or a change in a level ofactivity of individuals and/or groups of people over time. Theformula/lookup table may be based at least in part on or derived fromhistorical sales data, which data may relate to any of the variables orparameters used in the formula.

Again using the example of a restaurant selling pizza by the slice, therestaurant may have derived a formula based on historical data for thelikelihood that an individual within a half-mile radius of therestaurant will purchase a slice of pizza within the restaurant withinthe next ten minutes. According to the formula, the likelihood may beestimated as the sum total of the seven parameters discussed above withregard to the Population Activity Map process with the addition of aneighth parameter in the form of historical transaction rates forindividual regions in individual time periods. For instance, therestaurant may retrieve from its own records that 43 slices of pizzawere sold in the same second Saturday in May in the same 12:10 pm to12:20 pm time period. The restaurant's previous estimate of 37 slices tobe sold may be biased toward the historical transaction rate with aweighting factor to be selected by the restaurant. For instance, if therestaurant were to assign the historical transaction rate with aweighting factor of 0.5, then the estimate of 37 slices may be movedhalf-way toward the historical rate of 43 slices to arrive at a finalestimate of 40 slices to be sold in the ten minute period.

A further operation of the transaction likelihood map process mayinvolve providing goods and/or services by: determining the appropriatetime to provide the goods and/or services based on transactionlikelihood, determining the appropriate location for individual serviceproviders based on the transaction likelihood, and/or determining theappropriate quantity of service providers, service activity, and/orgoods to be provided at individual locations of interest based on thetransaction likelihood proximate to those locations. Using the exampleof a taxi company operating in the region of the above-described pizzarestaurant, the company may decide to send additional taxis to theregion within five minutes if it is determined that there is anincreasing likelihood of transaction within the region. In addition, thetaxi company may decide to send the additional taxis specifically to anarea between the restaurant district and the theater based on atransaction likelihood in that particular area. Further, the taxicompany may decide on a number of taxis to dispatch to the region basedon transaction likelihood in the region.

With reference to FIG. 3, depicted is a block diagram illustrating anexample computing device 300 that is arranged for telemetrics basedlocation and/or tracking implementations in accordance with at leastsome embodiments of the present disclosure. In a very basicconfiguration 301, computing device 300 typically includes one or moreprocessors 310 and system memory 320. A memory bus 330 may be used forcommunicating between the processor 310 and the system memory 320.

Depending on the desired configuration, processor 310 may be of any typeincluding but not limited to a microprocessor (μP), a microcontroller(μC), a digital signal processor (DSP), or any combination thereof.Processor 310 may include one more levels of caching, such as a levelone cache 311 and a level two cache 312, a processor core 313, andregisters 314. The processor core 313 may include an arithmetic logicunit (ALU), a floating point unit (FPU), a digital signal processingcore (DSP Core), or any combination thereof. A memory controller 315 mayalso be used with the processor 310, or in some implementations thememory controller 315 may be an internal part of the processor 310.

Depending on the desired configuration, the system memory 320 may be ofany type including but not limited to volatile memory (such as RAM),non-volatile memory (such as ROM, flash memory, etc.) or any combinationthereof. System memory 320 may include an operating system 321, one ormore applications 322, and program data 324. Application 322 may includea mobile device telemetrics data aggregation algorithm 323 that isimplemented to add together and integrate the identities, demographicinformation and/or historical information related to the mobile deviceswithin the geographic region. Program Data 324 may include historicalmobile device telemetrics data 325. In some embodiments, application 322may be arranged to operate with program data 324 on an operating system321 to effectuate the aggregation of the mobile device data. Thisdescribed basic configuration is illustrated in FIG. 3 by thosecomponents within dashed line 301.

Computing device 300 may have additional features or functionality, andadditional interfaces to facilitate communications between the basicconfiguration 301 and any required devices and interfaces. For example,a bus/interface controller 340 may be used to facilitate communicationsbetween the basic configuration 301 and one or more data storage devices350 via a storage interface bus 341. The data storage devices 350 may beremovable storage devices 351, non-removable storage devices 352, or acombination thereof. Examples of removable storage and non-removablestorage devices include magnetic disk devices such as flexible diskdrives and hard-disk drives (HDD), optical disk drives such as compactdisk (CD) drives or digital versatile disk (DVD) drives, solid statedrives (SSD), and tape drives to name a few. Example computer storagemedia may include volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage ofinformation, such as computer readable instructions, data structures,program modules, or other data.

System memory 320, removable storage 351 and non-removable storage 352are all examples of computer storage media. Computer storage mediaincludes, but is not limited to, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which maybe used to store the desired information and which may be accessed bycomputing device 300. Any such computer storage media may be part ofdevice 300.

Computing device 300 may also include an interface bus 342 forfacilitating communication from various interface devices (e.g., outputinterfaces, peripheral interfaces, and communication interfaces) to thebasic configuration 301 via the bus/interface controller 340. Exampleoutput devices 360 include a graphics processing unit 361 and an audioprocessing unit 362, which may be configured to communicate to variousexternal devices such as a display or speakers via one or more A/V ports363. Example peripheral interfaces 370 include a serial interfacecontroller 371 or a parallel interface controller 372, which may beconfigured to communicate with external devices such as input devices(e.g., keyboard, mouse, pen, voice input device, touch input device,etc.) or other peripheral devices (e.g., printer, scanner, etc.) via oneor more I/O ports 373. An example communication device 380 includes anetwork controller 381, which may be arranged to facilitatecommunications with one or more other computing devices 390 over anetwork communication via one or more communication ports 382. Thecommunication connection is one example of a communication media.Communication media may typically be embodied by computer readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave or other transportmechanism, and includes any information delivery media. A “modulateddata signal” may be a signal that has one or more of its characteristicsset or changed in such a manner as to encode information in the signal.By way of example, and not limitation, communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, radio frequency (RF), infrared (IR) andother wireless media. The term computer readable media as used hereinmay include both storage media and communication media.

Computing device 300 may be implemented as a portion of a small-formfactor portable (or mobile) electronic device such as a cell phone, apersonal data assistant (PDA), a personal media player device, awireless web-watch device, a personal headset device, an applicationspecific device, or a hybrid device that include any of the abovefunctions. Computing device 300 may also be implemented as a personalcomputer including both laptop computer and non-laptop computerconfigurations or as a server or mainframe.

According to one embodiment, computing device 300 is connected in anetworking environment such that the processor 310, application 322and/or program data 324 may perform with or as a computer system inaccordance with embodiments herein.

FIG. 4 is a flowchart showing the operation of an example populationactivity mapping method 400, as contemplated by at least someembodiments of the present disclosure. The example embodiments mayinclude one or more of processing operations 402, 404, 406, 408 and 410.Processing begins at operation 402, where wireless mobile devicescarried by people within a geographic region are detected at at leasttwo different points in time. For example, individual mobile devices 112may be detected at at least two different times by use of GPS satellites114, cellular base stations, and/or a wireless access point, as used increating a Hot Spot in a public setting. In a specific example, mobiledevices 112 may ascertain their locations within a geographic region byuse of GPS satellites 114, and may report their locations to centraloffice 116.

Processing flows from operation 402 to operation 404. Operation 404 mayinclude providing input data based upon the detecting operation. Forexample, mobile devices 112 may on a periodic basis transmit to centraloffice 116 data including their global geographic coordinates andidentities.

In operation 406, a distance and speed at which the mobile devicestravel within the geographic region may be discerned. For example, basedon changes in the reported location of a mobile device 112 over time, aprocessor within central office 116 may calculate a distance and speedat which the mobile device travels.

In a next operation 408, a time at which salable output is to be madeavailable to the people, a location at which salable output is to bemade available to the people, and/or an amount of salable output to bemade available to the people may be determined. For example, based atleast in part on a distance and speed at which mobile devices within theregion are moving, goods and/or services may be provided at a time andplace calculated to result in a high level of sales of the goods and/orservices. Moreover, the goods and/or services may be provided inquantities and manpower levels calculated to satisfy the demand for thegoods and/or services. The determination of times, locations and amountsmay be made by a processor of a networked computer at central office 116or by a processor of a networked computer at provider 118.

Next, in operation 410, a result of the determining operation may bepresented on a user interface. For instance, a suggested time, location,and quantities for provider 118 to offer his goods/servicers may beprinted on a printer, displayed on a monitor, and/or spoken on audiospeakers that are associated with provider 118 and that are connected tothe computer network.

In an example embodiment, an arrangement 100 for determining andcollecting the location of a mobile device, and/or telemetrics-basedlocation and tracking arrangement 200 may be configured to implement themethod of FIG. 4.

A map 500 that may be used in at least some embodiments of the presentdisclosure is illustrated in FIG. 5. The present locations of mobiledevices 112 ₁₋₄ are indicated in solid rectangles, while previouslocations of mobile devices 112 ₁₋₄ are indicated in dashed rectangles.A static population density map may include the present locations ofmobile devices 112 ₁₋₄ disposed within a geographic area 522 of acentral office 116. The mobile devices may be arranged to report theirthen-current locations to central office 116, as indicated by the arrowsfrom the mobile devices to central office 116.

In creating a population activity map, the previous locations of each ofthe mobile devices, as indicated by the dashed rectangles, may becompared to its present location. These displacements of each of theindividual mobile devices may be determined in conjunction with thelength of time in which the displacement occurred. Thus, speeds anddirections of movement of the mobile devices may be determined. In someembodiments, movements of mobile devices between different geographicareas 522 may be tracked.

In order to simplify the discussion of the above-described exampleembodiments, it may have been assumed that each of the mobile devicessubscribes to a locating service or could be located by other means. Inactuality, however, in any of the above-described embodiments, it may bepossible to locate only a known percentage of the population of mobiledevices. Accordingly, an estimation function may be used to predict theactual customer base. More particularly, since not everyone has aparticipating cell phone/PDA, information about subscription rates maybe used to estimate an actual customer population. In a simpleembodiment, this may be a single scaling factor. For example, if it isestimated that 10% of the population has a participating, trackabledevice, and one hundred devices are actually detected within a region,it may be estimated that there are 1000 people in the region. In a moresophisticated embodiment, this scaling function may be performed foreach identifiable demographic, and the estimated total number of peoplewithin each demographic may be added together. That is, assuming atrackable participation rate T_(i) for each independent segment i of thepopulation, and P_(i) detected individuals within each segment, then thetotal population could be estimated as the sum of P_(i)/T_(i) for allvalues of i.

The determination of the speed and direction of movement of theindividual wireless devices have been described in at least some of theabove example embodiments as being calculated by a computer network.However, it is also possible within the scope of the invention for theindividual wireless devices to calculate their own speed and directionof movement and then report such speed and direction to a computernetwork.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely examples, and that in fact many other architectures may beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality may be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated may also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated may also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art may translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

What is claimed is:
 1. A method for mapping population activity, themethod comprising: detecting wireless mobile devices within a geographicregion at two or more different points in time; discerning a location,speed and direction of the wireless mobile devices within the geographicregion to discern a particular location toward which the wireless mobiledevices are moving; determining, based upon the location, speed,direction and the particular location toward which the wireless mobiledevices are moving: a time at which goods or services are to be madeavailable; a location at which the goods or services are to be madeavailable; and a quantity of the goods or services to be made available,the determining being performed by one or more processors of a computernetwork; and presenting a result of the determining on a user interfaceof the computer network.
 2. The method of claim 1, wherein individualwireless mobile devices include a processor, a transmitter, and areceiver; and wherein the detecting is performed by use of one or moreof a wireless access point, a GPS satellite, and/or a base station. 3.The method of claim 2, wherein the discerning is performed by either atleast one of the one or more processors of the computer network, or theprocessor of at least one of the individual wireless mobile devices. 4.The method of claim 1, further comprising collecting demographicinformation associated with at least some of the wireless mobiledevices, wherein the determining is at least partially dependent uponthe demographic information.
 5. The method of claim 1, furthercomprising retrieving historical information from a memory device,wherein the determining is at least partially dependent upon thehistorical information.
 6. The method of claim 1, wherein thedetermining is at least partially dependent upon one or more of apresent time-of-day, a present day-of-the-week, and/or a presentcalendar date.
 7. The method of claim 1, wherein the determining is atleast partially dependent upon a change over time in a number of thewireless mobile devices that are detected.